超敏感的Pa级持久机电发光材料向全光学神经突触进行触觉视觉信息识别和记忆
Zhijie Ye1, Shuangqiang Fang1, Tiancheng Zhang1,2
1College of Optics and Electronic Technology, China Jiliang University, Hangzhou, 310018, China.
Advanced materials (Deerfield Beach, Fla.)
|October 4, 2025
概括
研究人员开发了一种新的机械发光 (ML) 材料,用于节能,光驱动的人工智能. 这一突破使Pa级触觉感知和先进的神经形态计算应用成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 神经形态工程的神经形态工程
- 发光的光度是非常的低.
背景情况:
- 机械发光 (ML) 为全光学神经形态计算提供了一个被动的,自我恢复的途径.
- 现有的ML材料在低值感知和多层信号传输方面面临限制,原因是响应值高和单模式发光.
研究的目的:
- 为神经形态应用设计一种具有增强灵敏度和信号清晰度的新型ML材料.
- 为了克服当前ML材料的局限性,用于亚kPa感知和多层神经传输.
主要方法:
- 在Sr2SiO4:Eu2+ (LSSO) 中使用Li+/Dy3+联合剂来实施缺陷工程策略.
- 优化氧气空缺和抑制空缺以改善ML属性.
- 研究了材料对力,光和热的反应,以实现突触模拟.
主要成果:
- 实现了创纪录的低ML值72Pa,这是第一个没有外部电力或弹性质修改的Pa级系统.
- 经证实持续7秒的ML,信号噪声比商业标准高15.6倍.
- 展示了微米级成像分辨率 (≈200微米) 和36小时内存容量,精度比埃宾豪斯曲线高出209%.
结论:
- 开发的LSSO材料显著推进了全光学突触的设计.
- 建立了ML和神经形态工程之间的关键联系,以节能,光驱动的人工智能.
- 为下一代触觉视觉计算系统铺平了道路.
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